TY - GEN
T1 - Prediction of meteorological parameters
T2 - 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
AU - Bhattacharjee, Shrutilipi
AU - Das, Monidipa
AU - Ghosh, Soumya K.
AU - Shekhar, Shashi
N1 - Publisher Copyright:
© 2016 ACM.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - Meteorological parameters are often considered as crucial factors for climatological pattern analysis. Predictions of these parameters have been studied extensively in the field of remote sensing and GIS. It is one of the most critical steps involved in most of the meteorological data mining process. Spatial interpolation is an eficient technique to yield minimal error in prediction. From existing literatures, it is evident that the land-use/land-cover (LULC) distribution of the terrain inuences these parameters in a varying manner and it is important to model their behaviour for climatological analyses. However, this semantic LULC knowledge of the terrain is generally ignored in the prediction process of the meteorological parameters. Recently, we have proposed a new spatial interpolation technique, namely semantic kriging (SemK) [3, 5, 7], which considers the semantic LULC knowledge for land-atmospheric interaction modeling and incorporates it into the existing interpolation process for better accuracy. However, the a-priori correlation analysis of SemK ignores the effect of other nearby LULC classes on each other. This article presents a new variant of SemK, namely a-posterior probabilistic Bayesian SemK (BSemK), which extends the a-priori correlation analysis of SemK with a-posterior probabilistic analysis. The proposed approach provides more accurate estimation of the parameters. Experimentation with LST data advocates the effcacy of the proposed approach compared to the a-priori SemK and other existing interpolation techniques.
AB - Meteorological parameters are often considered as crucial factors for climatological pattern analysis. Predictions of these parameters have been studied extensively in the field of remote sensing and GIS. It is one of the most critical steps involved in most of the meteorological data mining process. Spatial interpolation is an eficient technique to yield minimal error in prediction. From existing literatures, it is evident that the land-use/land-cover (LULC) distribution of the terrain inuences these parameters in a varying manner and it is important to model their behaviour for climatological analyses. However, this semantic LULC knowledge of the terrain is generally ignored in the prediction process of the meteorological parameters. Recently, we have proposed a new spatial interpolation technique, namely semantic kriging (SemK) [3, 5, 7], which considers the semantic LULC knowledge for land-atmospheric interaction modeling and incorporates it into the existing interpolation process for better accuracy. However, the a-priori correlation analysis of SemK ignores the effect of other nearby LULC classes on each other. This article presents a new variant of SemK, namely a-posterior probabilistic Bayesian SemK (BSemK), which extends the a-priori correlation analysis of SemK with a-posterior probabilistic analysis. The proposed approach provides more accurate estimation of the parameters. Experimentation with LST data advocates the effcacy of the proposed approach compared to the a-priori SemK and other existing interpolation techniques.
KW - Bayesian analysis
KW - Land-atmospheric interaction
KW - Meteorological parameters
KW - Prediction
KW - Semantic Kriging
KW - Spatial interpolation
UR - http://www.scopus.com/inward/record.url?scp=85011067059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011067059&partnerID=8YFLogxK
U2 - 10.1145/2996913.2996968
DO - 10.1145/2996913.2996968
M3 - Conference contribution
AN - SCOPUS:85011067059
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
A2 - Renz, Matthias
A2 - Ali, Mohamed
A2 - Newsam, Shawn
A2 - Renz, Matthias
A2 - Ravada, Siva
A2 - Trajcevski, Goce
PB - Association for Computing Machinery
Y2 - 31 October 2016 through 3 November 2016
ER -